Recommendations of products or services are used by computing devices to expose users to items with which the user may not be aware but may have a potential interest. For example, recommendations involving products may be provided by computing devices to a user of a shopping service system or recommendations of movies may be provided to a user of an online streaming service system, and so on. Recommendations have become one of the major drivers as part of a decision making process by users to interact with digital content and even in some places has supplanted search in locating products or services of interest.
Conventional recommendations are generated by computing devices through use of models built from data that describes past interactions of users with items. The models are used by the computing devices along with information regarding current interactions with items to provide recommendations based on these interactions. For example, for a user that is currently viewing a dryer, a dryer vent hose recommendation may be provided by the computing devices to the user.
Conventional models used by computing devices to provide recommendations are built based on previous user interactions. These user interactions may involve items that were viewed together, which are known as item view-view relationships. These user interactions may also involve items that were viewed and ultimately bought, which are known as item view-bought relationships. Another example of user interactions involve items that were bought together, which are known as item bought-bought relationships. Models built using these relationships are often referred to as co-occurrence models. A drawback of these types of models, however, is that the models are unable to distinguish why interaction occurred between two items. Consequently, convention techniques that rely on these models may be inaccurate. For example, if a power cord is popular with users it may be provided as a recommendation in conventional techniques, not because the power cord is similar to viewed items, but because it is a popular item. Accordingly, conventional techniques used by computing devices may fail to provide useful item recommendations due to a failure by the computing devices to address relationships involving similarities between items.